Knowledge Worker productivity and AI - DBR 002

Do Busy Right

I discuss a definition of Knowledge Work and illustrate it with four (plus one) categories of Knowledge Work. I also talk about the implications of AI on each category.

• Knowledge Work is using your brain/attention to produce new or altered information
• Four categories
• Factual knowledge is stored facts that can be recalled and used. Google is doing a pretty good job of making this kind of knowledge ubiquitous and thus irrelevant.
• Process knowledge is stored processes of transforming or organizing information that can be recalled and reused. An example is how to create a routine report. To the degree that a process is truly repetitive, we employ computer programs to replicate this category of knowledge work.
• Understanding knowledge is closest to experience and considers which tool, process, or system should be used to address a specific kind of knowledge work problem.
• Knowledge creation is the generation and description of new knowledge. This is the realm of academics, science, and engineering.
• Plus one category
• Problem solving is different. It encompasses the other four categories (to the degree that they are available to the problem solver) and produces outputs that are novel, at least in the problem solver's experience. In another sense, it also underlies the other four. Example: if factual knowledge is lacking, we can use problem solving to find the fact.
• Knowledge worker productivity
• Peter Drucker posed the notion that the primary challenge of 21st century management is to improve the productivity of knowledge workers. His example is the 20th century's vast improvement of the productivity of manual labor.
• This is not yet a clearly solved problem, although he posed it about 60 years ago.
• What about AI? Is it evil, or is it helping us be "more productive" in some categories of knowledge work?
• Google has come a long way in giving everyone factual knowledge.
• Computer programs are helping us with process knowledge.
• Chat GPT can help us with the understanding category, but only insofar as the needed output is currently known and already exists in the interwebs.
• Knowledge creation doesn't seem to be addressed by current technology. Further, it is difficult to see how new knowledge can be created just from summing up all existing knowledge.
• Problem solving is similarly situated. There is a semi-random, creative component of problem solving that, at present, seems to not be cleanly mimicked by technology.
• Examples from the notion of 'affordances' in existing technology and/or tools.
• Problem solving is sometimes subject to the human notion of 'satisficing'. Depending on the severity and cost of the problem, we may use less-than-the-perfect-tool to solve it. Sometimes, the best thing to do is to pull the screw with a hammer.
• Do we, as knowledge workers, need to embrace AI or fight it?
• Will AI replace my knowledge work job?
• How does Attention Compass act as the 'Knowledge Worker Operating System'?

Think about these ideas as you learn to apply (or avoid applying) AI in your own work. Hit subscribe to be informed of new episodes. Happy to help you think about Attention Compass for your own knowledge work. Need more info? Go to dobusyright.com. Connect with me on Linked In linkedin.com/larrytribble. Or, shoot me a thought at [e-mail]. Do Busy Right, Larry Tribble, Ph.D.